Packages

package root

Definition Classes: root

package com

Definition Classes: root

package here

Definition Classes: com

package platform

Definition Classes: here

package data

Definition Classes: platform

package processing

This package provides the Data Processing Library for building distributed data processing applications.

A Runner both implements the interface with the environment for an application to run, and starts the application. The application, in turn, is driven by a Driver, that controls and performs the distributed processing.

Choose a Runner best suited for the environment where the application runs.

The Driver performs one of more tasks which read layers from input catalogs and write to one or more layers of an output catalog.

The main entry point in the processing library is the com.here.platform.data.processing.driver.DriverBuilder class where you can add different kinds of tasks to the driver. The driver runs the tasks, and commits the final results to the output catalog.

Tasks are implemented using one or more compilers.

The simplest compiler is the direct compiler which maps each input tile to N output tiles. The application needs to define com.here.platform.data.processing.compiler.Direct1ToNCompiler.

Other more complex compilation patterns are based on some kind of dependency tracking between input partitions and output partitions.

The processing Library supports the following patterns:

- com.here.platform.data.processing.compiler.NonIncrementalCompiler: non-incremental compilation only - com.here.platform.data.processing.compiler.DepCompiler: non-incremental dependency calculation and incremental compilation - com.here.platform.data.processing.compiler.IncrementalDepCompiler: incremental dependency calculation and compilation - com.here.platform.data.processing.compiler.Direct1ToNCompiler: incremental compilation where every output tile depends only on one input tile, and this mapping is independent from tile content - com.here.platform.data.processing.compiler.DirectMToNCompiler: incremental compilation where every output tile depends on multiple input tiles, and this mapping is independent from tile content - com.here.platform.data.processing.compiler.MapGroupCompiler: incremental compilation where every output tile can depend on multiple input tiles, and this mapping depend on the tile content - com.here.platform.data.processing.compiler.RefTreeCompiler: fully-managed two phases incremental compilation that can resolve references between input partitions. Input/Output dependency management is implemented and the developer doesn't need to provide this logic

The application's main object normally mixes in the a runner trait (like PipelineRunner) to setup the Driver, and interfaces with the environment where the application is run. See the Main classes in the example compilers for more details.

com.here.platform.data.processing.catalog, com.here.platform.data.processing.blobstore, and com.here.platform.data.processing.publisher contain utilities for accessing catalogs and payloads in a Spark-friendly way, providing an RDD-based abstraction over data and metadata. These classes are used by the processing library, but can also be used independently.

Definition Classes: data

package java

This package provides Java bindings for the Data Processing Library, to build distributed data processing applications in Java.

Choose a Runner best suited for the environment where the application runs.

The Driver performs one of more tasks which read layers from input catalogs and write to one or more layers of an output catalog.

The main entry point in the processing library is the com.here.platform.data.processing.java.driver.DriverBuilder class where you can add different kinds of tasks to the driver. The driver runs the tasks, and commits the final results to the output catalog.

Tasks are implemented using one or more compilers.

The simplest compiler is the direct compiler which maps each input tile to N output tiles. The application needs to implement a com.here.platform.data.processing.java.compiler.Direct1ToNCompiler.

Other more complex compilation patterns are based on different types of dependency tracking between input partitions and output partitions.

The processing Library supports the following patterns:

- com.here.platform.data.processing.java.compiler.NonIncrementalCompiler: non-incremental compilation only - com.here.platform.data.processing.java.compiler.DepCompiler: non-incremental dependency calculation and incremental compilation - com.here.platform.data.processing.java.compiler.IncrementalDepCompiler: incremental dependency calculation and compilation - com.here.platform.data.processing.java.compiler.Direct1ToNCompiler: incremental compilation where every output tile depends only on one input tile, and this mapping is independent from tile content - com.here.platform.data.processing.java.compiler.DirectMToNCompiler: incremental compilation where every output tile can depend on multiple input tiles, and this mapping is independent from tile content - com.here.platform.data.processing.java.compiler.MapGroupCompiler: incremental compilation where every output tile can depend on multiple input tiles, and this mapping depend on the tile content - com.here.platform.data.processing.java.compiler.RefTreeCompiler: fully-managed two phases incremental compilation that can resolve references between input partitions. Input/Output dependency management is implemented and the developer doesn't need to provide this logic

The application's main object normally extends a runner class (like PipelineRunner) to setup the Driver, and interfaces with the environment where the application runs. For more details, see the Main classes in the example compilers.

com.here.platform.data.processing.java.catalog, com.here.platform.data.processing.java.blobstore, and com.here.platform.data.processing.java.publisher contain utilities for accessing catalogs and payloads in a Spark-friendly way, providing an RDD-based abstraction over data and metadata. These classes are used by the processing library, but can also be used independently.

Definition Classes: processing

package compiler

Definition Classes: java

package direct

Definition Classes: compiler

package mapgroup

Definition Classes: compiler

package reftree

Definition Classes: compiler

CompileOut1To1Fn

CompileOut1ToNFn

CompileOutFn

DepCompiler

Direct1ToNCompiler

DirectMToNCompiler

IncrementalDepCompiler

InputLayers

InputOptPartitioner

InputPartitioner

MapGroupCompiler

NonIncrementalCompiler

OutputLayers

OutputOptPartitioner

OutputPartitioner

RefTreeCompiler

com.here.platform.data.processing.java.compiler

RefTreeCompiler

trait RefTreeCompiler[T] extends InputLayers with InputOptPartitioner with ResolveInFn with CompileInFn[T] with OutputLayers with OutputOptPartitioner with CompileOutFn[T]

A RefTreeCompiler allows full and incremental compilation with complex reference structures. A condition is that all references of a layer can be calculated purely from the source data of this layer using a resolve function, which just gets the meta data of one partition as an input. The structure of the references has to be predefined in a reftree.RefTree object.

Apart from the reference resolution pre-phase described above, the compilation itself is split in two phases, front-end and backend, as in other compilers, similar to MapGroupCompiler.

In the first phase, compileIn from reftree.CompileInFn is called for every partition, with the full list of meta data for all of its referenced partitions. This method returns one or more values of type T for every impacted output partition.

The compileIn function for the first phase of the compilation is defined in traits that extend reftree.CompileInFn. One of these need to be mixed in, like reftree.CompileInFnWithRefs or reftree.CompileInFnWithRefsReturnsReferences.

In the second phase, a method from one of the CompileOutFn traits is called for every output partition where the first phase provided at least one element of T. Elements coming from various input partitions are grouped together and provided as input of compilation for each output partition.

T: The custom type of the values passed between front-end and back-end

Note: The implementation must be scala.Serializable as this is copied to workers and run inside Spark map functions.
,
This is a Java friendly version of com.here.platform.data.processing.compiler.RefTreeCompiler.
See also: extended interfaces for more details

Linear Supertypes

CompileOutFn[T], OutputOptPartitioner, OutputLayers, CompileInFn[T], ResolveInFn, Serializable, Serializable, InputOptPartitioner, InputLayers, AnyRef, Any

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RefTreeCompiler
CompileOutFn
OutputOptPartitioner
OutputLayers
CompileInFn
ResolveInFn
Serializable
Serializable
InputOptPartitioner
InputLayers
AnyRef
Any

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Abstract Value Members

abstract def inLayers: Map[String, Set[String]]
Layers of the input catalogs that should be queried and provided to the compiler, grouped by input catalog and identified by catalog id and layer ID.
Layers of the input catalogs that should be queried and provided to the compiler, grouped by input catalog and identified by catalog id and layer ID.

Definition Classes
InputLayers
abstract def inPartitioner(parallelism: Int): Option[PartitionerOfKey]
If the returned com.here.platform.data.processing.java.Java.Option is Empty, the Executor will use a default partitioner, if it is defined then the given partitioner will be applied when querying the input catalogs.
If the returned com.here.platform.data.processing.java.Java.Option is Empty, the Executor will use a default partitioner, if it is defined then the given partitioner will be applied when querying the input catalogs.
parallelism
the parallelism of the partitioner
returns
the input optional partitioner with the given parallelism

Definition Classes
InputOptPartitioner
abstract def outLayers: Set[String]
Layers that are expected to be produced by the compiler.
Layers that are expected to be produced by the compiler.

Definition Classes
OutputLayers
abstract def outPartitioner(parallelism: Int): Option[PartitionerOfKey]
If the returned com.here.platform.data.processing.java.Java.Option is Empty, the Executor will use a default partitioner, if it is defined then the given partitioner will be applied when querying the output catalog and producing data to be published.
If the returned com.here.platform.data.processing.java.Java.Option is Empty, the Executor will use a default partitioner, if it is defined then the given partitioner will be applied when querying the output catalog and producing data to be published.
parallelism
the parallelism of the partitioner
returns
the output optional partitioner with the given parallelism

Definition Classes
OutputOptPartitioner
abstract def refStructure: RefTree
The refStructure defines the maximum reference tree of the source layers.
The refStructure defines the maximum reference tree of the source layers.

See also
com.here.platform.data.processing.java.compiler.reftree.RefTree for details.
abstract def resolveFn(src: Java.Pair[InKey, InMeta]): Map[String, Set[InKey]]
Resolves the list of partitions in all the reference target layers based on the a source partition.
Resolves the list of partitions in all the reference target layers based on the a source partition. The returned name values for the references must be aligned with the values returned by com.here.platform.data.processing.java.compiler.RefTreeCompiler.refStructure.
src
the source partition key and metadata

Definition Classes
ResolveInFn

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( ... ) @native()
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
Annotations
@native()
def hashCode(): Int

Definition Classes
AnyRef → Any
Annotations
@native()
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit

Definition Classes
AnyRef
Annotations
@native()
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... ) @native()

Packages

RefTreeCompiler

trait RefTreeCompiler[T] extends InputLayers with InputOptPartitioner with ResolveInFn with CompileInFn[T] with OutputLayers with OutputOptPartitioner with CompileOutFn[T]

Abstract Value Members

Concrete Value Members

Inherited from CompileOutFn[T]

Inherited from OutputOptPartitioner

Inherited from OutputLayers

Inherited from CompileInFn[T]

Inherited from ResolveInFn

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

RefTreeCompiler 

trait RefTreeCompiler[T] extends InputLayers with InputOptPartitioner with ResolveInFn with CompileInFn[T] with OutputLayers with OutputOptPartitioner with CompileOutFn[T]

Abstract Value Members

Concrete Value Members

Inherited from CompileOutFn[T]

Inherited from OutputOptPartitioner

Inherited from OutputLayers

Inherited from CompileInFn[T]

Inherited from ResolveInFn

Inherited from Serializable

Inherited from Serializable

Inherited from InputOptPartitioner

Inherited from InputLayers

Inherited from AnyRef

Inherited from Any

Ungrouped

RefTreeCompiler